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Research

Understanding Affinity Maturation in de novo designed miniprotein binders.

in silico Site Saturation Mutagenesis of Hemagglutinin Miniprotein Binders

Computational protein design efforts continue to make remarkable advances, yet the discovery of high-affinity binders typically requires large-scale experimental screening of site-saturated mutant (SSM) libraries. Here, we explore how massively parallel free energy methods can be used for in silico affinity maturation of de novo designed binding proteins. Using an expanded ensemble (EE) approach, we perform exhaustive relative binding free energy calculations for SSM variants of three miniproteins designed to bind influenza A H1 hemagglutinin by Chevalier et al. (2017). We compare our predictions to experimental ΔΔG values inferred from a Bayesian analysis of the high-throughput sequencing data, and to state-of-the-art predictions made using the Flex ddG Rosetta protocol. A systematic comparison reveals prediction accuracies around 2 kcal/mol, and identifies net charge changes, large numbers of alchemical atoms, and slow side chain conformational dynamics as key contributors to the uncertainty of the EE predictions. Flex ddG predictions are more accurate on average, but highly conservative. In contrast, EE predictions can better classify stabilizing and destabilizing mutations. We also explored the ability of SSM scans to rationalize known affinity-matured variants containing multiple mutations, which are non-additive due to epistatic effects. Simple electrostatic models fail to explain non-additivity, but observed mutations are found at positions with higher Shannon entropies. Overall, this work suggests that simulation-based free energy methods can provide predictive information for in silico affinity maturation of designed miniproteins, with many feasible improvements to the efficiency and accuracy within reach.

Using multiensemble Markov models to investigate designed miniprotein binding to H1N1 hemagglutinin.

Hyperstable miniproteins can be de novo designed to tightly bind protein targets, a promising new avenue for treating infectious disease. The Baker lab at University of Washington has \textit{de novo designed} high affinity miniproteins that bind H1N1 hemagglutinin stem protein (HA2), inhibiting the conformation change of the fusion peptide region necessary for cellular infection. Ultimately, an exhaustive affinity maturation process improved binder dissociation constants by at least two orders of magnitude. The binders all contain a conserved hydrophobic binding motif however, the affinity matured variants have mutations at non-interfacial residues with no clear biophysical interpretation as to why they enhance binding affinity so dramatically. As proteins and their complexes exist as dynamic ensembles, we investigated these binding reactions using GPU-accelerated simulations of wild type and affinity matured A8, A13, and A18 HA2 binders on the Folding@home distributed computing platform (FAH). Analysis of these simulations using Markov state models (MSMs) shows that the affinity matured binders have faster $k_{on}$s than the wild type binders and prefer different bound state conformations than the wild type. To promote unbinding transitions, we used simulations with a repulsive gaussian bias between the miniproteins and HA2. We used the TRAM algorithm to build muliensemble Markov models in an attempt to more completely model the binding reactions of these miniproteins. This work moves towards a better understanding of the conformational dynamics of miniprotein-protein interactions and how mutations can effect those interactions.

FOX01

FOXO1, a member of the family of winged-helix motif Forkhead box (FOX) transcription factors, is the most abundantly expressed FOXO member in mature B cells. Sequencing of diffuse large B-cell lymphoma (DLBCL) tumors and cell lines identified specific mutations in the forkhead domain linked to loss of function. Differential scanning calorimetry and thermal shift assays were used to characterize how eight of these mutations affect the stability of the FOX domain. Mutations L183P and L183R were found to be particularly destabilizing. Electrophoresis mobility shift assays show these same mutations also disrupt FOXO1 binding to their canonical DNA sequences, suggesting that the loss of function is due to destabilization of the folded structure. Computational modeling of the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3. Although FEP can qualitatively predict the destabilization from L183 mutations, we find that a simple hydrophobic transfer model, combined with estimates of unfolded-state solvent-accessible surface areas from molecular simulations, is able to more accurately predict changes in folding free energies due to mutations. These results suggest that the atomic detail provided by simulations is important for the accurate prediction of mutational effects on folding stability. Corresponding disease-associated mutations in other FOX family members support further experimental and computational studies of the folding mechanism of FOX domains.